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Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel...

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Autores principales: Tsai, Yu-Shuen, Aguan, Kripamoy, Pal, Nikhil R., Chung, I-Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3164723/
https://www.ncbi.nlm.nih.gov/pubmed/21909426
http://dx.doi.org/10.1371/journal.pone.0024259
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author Tsai, Yu-Shuen
Aguan, Kripamoy
Pal, Nikhil R.
Chung, I-Fang
author_facet Tsai, Yu-Shuen
Aguan, Kripamoy
Pal, Nikhil R.
Chung, I-Fang
author_sort Tsai, Yu-Shuen
collection PubMed
description Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases.
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spelling pubmed-31647232011-09-09 Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index Tsai, Yu-Shuen Aguan, Kripamoy Pal, Nikhil R. Chung, I-Fang PLoS One Research Article Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases. Public Library of Science 2011-09-01 /pmc/articles/PMC3164723/ /pubmed/21909426 http://dx.doi.org/10.1371/journal.pone.0024259 Text en Tsai et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Tsai, Yu-Shuen
Aguan, Kripamoy
Pal, Nikhil R.
Chung, I-Fang
Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index
title Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index
title_full Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index
title_fullStr Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index
title_full_unstemmed Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index
title_short Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index
title_sort identification of single- and multiple-class specific signature genes from gene expression profiles by group marker index
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3164723/
https://www.ncbi.nlm.nih.gov/pubmed/21909426
http://dx.doi.org/10.1371/journal.pone.0024259
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